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The Energy Box : comparing locally automated control strategies of residential electricity consumption under uncertainty

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dc.contributor.advisor Richard C. Larson. en_US
dc.contributor.author Livengood, Daniel James en_US
dc.contributor.other Massachusetts Institute of Technology. Engineering Systems Division. en_US
dc.date.accessioned 2012-01-11T20:18:02Z
dc.date.available 2012-01-11T20:18:02Z
dc.date.copyright 2011 en_US
dc.date.issued 2011 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/68190
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2011. en_US
dc.description This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. en_US
dc.description Cataloged from student submitted PDF version of thesis. en_US
dc.description Includes bibliographical references (p. 109-121). en_US
dc.description.abstract The Energy Box is an always-on background processor automating the temporal management of one's home or small business electrical energy usage. Cost savings are achieved in a variety of environments, ranging from at pricing of electricity to real-time demand-sensitive pricing. Further cost savings derive from utilizing weather forecasts to manage local rooftop wind turbines or solar photovoltaics and/or to anticipate price swings from central utilities. The main motivation of this research is to design, construct and test a prototype software architecture for the Energy Box that can accommodate a wide variety of local energy management environments and user preferences. Under some scenarios, appliances can be optimally controlled one at a time, independent of each other. In other scenarios, coordinated control of appliances, either simultaneous or time-sequenced, provide better outcomes. Stochastic dynamic programming is the primary optimization engine. The optimization goal is to balance cost minimization with thermal comfort as specified by consumer preferences. The results demonstrate that the desired general energy management platform is feasible as well as desirable for saving money on electricity while maintaining comfort preferences. Scaling up to neighborhoods, towns and cities, a key contribution is improved understanding of single-home electricity demand dynamics induced by automated decisions. Further research will determine how such local automated decisions affect the broader smart grid with regard to resilience, stability and pricing. en_US
dc.description.statementofresponsibility by Daniel James Livengood. en_US
dc.format.extent 142 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Engineering Systems Division. en_US
dc.title The Energy Box : comparing locally automated control strategies of residential electricity consumption under uncertainty en_US
dc.title.alternative Comparing locally automated control strategies of residential electricity consumption under uncertainty en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Engineering Systems Division. en_US
dc.identifier.oclc 770698097 en_US


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